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Sergushichev AA, Loboda AA, Jha AK, Vincent EE, Driggers EM, Jones RG, Pearce EJ, Artyomov MN. GAM: a web-service for integrated transcriptional and metabolic network analysis. Nucleic Acids Res 2016; 44:W194-200. [PMID: 27098040 PMCID: PMC4987878 DOI: 10.1093/nar/gkw266] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Accepted: 04/04/2016] [Indexed: 01/14/2023] Open
Abstract
Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM (‘genes and metabolites’): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/
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Affiliation(s)
- Alexey A Sergushichev
- Computer Technologies Department, ITMO University, Saint Petersburg, 197101, Russia Department of Pathology & Immunology, Washington University in St. Louis, St.Louis, MO 63110, USA
| | - Alexander A Loboda
- Computer Technologies Department, ITMO University, Saint Petersburg, 197101, Russia
| | | | - Emma E Vincent
- Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
| | | | - Russell G Jones
- Goodman Cancer Research Centre, McGill University, Montreal, QC H3A 1A3, Canada Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Edward J Pearce
- Department of Immunometabolism, Max Planck Institute of Immunobiology and Epigenetics, Freiburg, D-79108, Germany
| | - Maxim N Artyomov
- Computer Technologies Department, ITMO University, Saint Petersburg, 197101, Russia Department of Pathology & Immunology, Washington University in St. Louis, St.Louis, MO 63110, USA
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Feussner I, Polle A. What the transcriptome does not tell - proteomics and metabolomics are closer to the plants' patho-phenotype. CURRENT OPINION IN PLANT BIOLOGY 2015; 26:26-31. [PMID: 26051215 DOI: 10.1016/j.pbi.2015.05.023] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 05/18/2015] [Accepted: 05/18/2015] [Indexed: 05/18/2023]
Abstract
The proteome and metabolome of the plant provide a wealth of additional information on plant-microbe interactions since they not only represent additional levels of regulation, but often they harbor the end products of regulatory processes. Proteomics has contributed to our understanding of plant-microbe research by increasing the spatial resolution of the analysis within the infected tissue, because components of the basal immunity were uncovered in the apoplast. Metabolomics has developed into a powerful approach to discover the role of small molecules during plant-microbe interactions in non-model plants since it does not depend on the availability of genome or transcriptome data. Moreover, novel molecules involved in systemic acquired resistance and the precursors for the formation of molecules that provide physical barriers to prevent spreading of pathogens were identified.
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Affiliation(s)
- Ivo Feussner
- Georg-August-University, Albrecht-von-Haller-Institute for Plant Sciences, Department of Plant Biochemistry, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany.
| | - Andrea Polle
- Georg-August University, Büsgen-Institute, Department for Forest Botany and Tree Physiology, Büsgenweg 2, 37077 Göttingen, Germany
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Kaever A, Landesfeind M, Feussner K, Mosblech A, Heilmann I, Morgenstern B, Feussner I, Meinicke P. MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data. Metabolomics 2015; 11:764-777. [PMID: 25972773 PMCID: PMC4419191 DOI: 10.1007/s11306-014-0734-y] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Accepted: 09/23/2014] [Indexed: 11/27/2022]
Abstract
A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (http://marvis.gobics.de), which features the seamless highly interactive filtering, combination, clustering, and visualization of omics data sets. The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.
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Affiliation(s)
- Alexander Kaever
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Manuel Landesfeind
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Kirstin Feussner
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
| | - Alina Mosblech
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
| | - Ingo Heilmann
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Ivo Feussner
- Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
| | - Peter Meinicke
- Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
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Sotelo-Silveira M, Chauvin AL, Marsch-Martínez N, Winkler R, de Folter S. Metabolic fingerprinting of Arabidopsis thaliana accessions. FRONTIERS IN PLANT SCIENCE 2015; 6:365. [PMID: 26074932 PMCID: PMC4444734 DOI: 10.3389/fpls.2015.00365] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 05/08/2015] [Indexed: 05/02/2023]
Abstract
In the post-genomic era much effort has been put on the discovery of gene function using functional genomics. Despite the advances achieved by these technologies in the understanding of gene function at the genomic and proteomic level, there is still a big genotype-phenotype gap. Metabolic profiling has been used to analyze organisms that have already been characterized genetically. However, there is a small number of studies comparing the metabolic profile of different tissues of distinct accessions. Here, we report the detection of over 14,000 and 17,000 features in inflorescences and leaves, respectively, in two widely used Arabidopsis thaliana accessions. A predictive Random Forest Model was developed, which was able to reliably classify tissue type and accession of samples based on LC-MS profile. Thereby we demonstrate that the morphological differences among A. thaliana accessions are reflected also as distinct metabolic phenotypes within leaves and inflorescences.
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Affiliation(s)
- Mariana Sotelo-Silveira
- Unidad de Genómica Avanzada (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN)Irapuato, México
- Laboratorio de Bioquímica, Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la RepúblicaMontevideo, Uruguay
| | - Anne-Laure Chauvin
- Unidad de Genómica Avanzada (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN)Irapuato, México
| | | | - Robert Winkler
- Department of Biotechnology and Biochemistry, CINVESTAV Unidad IrapuatoIrapuato, Mexico
- *Correspondence: Robert Winkler, Department of Biotechnology and Biochemistry, CINVESTAV Unidad Irapuato, Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36821 Irapuato, México
| | - Stefan de Folter
- Unidad de Genómica Avanzada (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN)Irapuato, México
- Stefan de Folter, Unidad de Genómica Avanzada (LANGEBIO), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Km. 9.6 Libramiento Norte, Carretera Irapuato-León, CP 36821 Irapuato, Guanajuato, Mexico
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